Datasets.
Our model is trained on two datasets: Places2 [1]} and our custom indoor dataset (Indoor). For Places2, we use about 1,800k images from various scenes as the training set, and 36,500 images as the validation. To better demonstrate the structural recovery, we collect 5,000 images from ShanghaiTech [2]} and 15,055 images from NYUDepthV2 [3]} to build the custom 20,055 Indoor training dataset. For the Indoor validation, we collect 1,000 images which are consist of 462 and 538 images from ShanhaiTech and NYUDepthV2 respectively. Places2 and Indoor can all be tested in both 256\(\times \) 256 and 512\(\times \) 512.
